| Deep learning for survival analysis: a review S Wiegrebe, P Kopper, R Sonabend, B Bischl, A Bender Artificial Intelligence Review 57 (3), 65, 2024 | 197 | 2024 |
| Protein intake and outcome of critically ill patients: analysis of a large international database using piece-wise exponential additive mixed models WH Hartl, P Kopper, A Bender, F Scheipl, AG Day, G Elke, H Küchenhoff Critical Care 26 (1), 7, 2022 | 56 | 2022 |
| A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses H Seibold, S Czerny, S Decke, R Dieterle, T Eder, S Fohr, N Hahn, ... PLoS One 16 (6), e0251194, 2021 | 38 | 2021 |
| Limitations of interpretable machine learning methods T Altmann, J Bodensteiner, C Dankers, T Dassen, N Fritz, S Gruber, ... Department of Statistics LMU Munich, 2020 | 36* | 2020 |
| Deeppamm: Deep piecewise exponential additive mixed models for complex hazard structures in survival analysis P Kopper, S Wiegrebe, B Bischl, A Bender, D Rügamer Pacific-Asia conference on knowledge discovery and data mining, 249-261, 2022 | 27* | 2022 |
| Semi-structured deep piecewise exponential models P Kopper, S Pölsterl, C Wachinger, B Bischl, A Bender, D Rügamer Survival Prediction-Algorithms, Challenges and Applications, 40-53, 2021 | 27 | 2021 |
| Deepregression: A flexible neural network framework for semi-structured deep distributional regression D Rügamer, C Kolb, C Fritz, F Pfisterer, P Kopper, B Bischl, R Shen, ... Journal of Statistical Software 105, 1-31, 2023 | 26* | 2023 |
| Relevance of protein intake for weaning in the mechanically ventilated critically ill: analysis of a large international database WH Hartl, P Kopper, L Xu, L Heller, M Mironov, R Wang, AG Day, G Elke, ... Critical Care Medicine 52 (3), e121-e131, 2024 | 5 | 2024 |
| How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression L Kook, C Kolb, P Schiele, D Dold, M Arpogaus, C Fritz, P Baumann, ... Proceedings of the Fortieth Conference on Uncertainty in Artificial …, 2024 | 4 | 2024 |
| Examining properness in the external validation of survival models with squared and logarithmic losses R Sonabend, J Zobolas, P Kopper, L Burk, A Bender arXiv:2212.05260, 2024 | 4* | 2024 |
| Correction: A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses H Seibold, S Czerny, S Decke, R Dieterle, T Eder, S Fohr, N Hahn, ... Plos one 17 (5), e0269047, 2022 | 4 | 2022 |
| Flexible estimation of complex effects in the context of competing risks survival analysis P Kopper | 2 | 2020 |
| Pammtools: piece-wise exponential additive mixed modeling tools for survival analysis 0.5. 4., 2020 A Bender, F Scheipl, P Kopper | 2 | |
| mlr3extralearners: Expanding the mlr3 Ecosystem with Community-Driven Learner Integration S Fischer, J Zobolas, R Sonabend, M Becker, M Lang, M Binder, ... Journal of Open Source Software 10 (115), 8331, 2025 | | 2025 |
| On Training Survival Models with Scoring Rules P Kopper, D Rügamer, R Sonabend, B Bischl, A Bender Joint European Conference on Machine Learning and Knowledge Discovery in …, 2025 | | 2025 |
| Bedeutung der Proteinzufuhr für die Entwöhnung bei mechanisch beatmeten Intensivpatient* innen–Analyse einer großen internationalen Datenbank WH Hartl, P Kopper, L Xu, L Heller, M Mironow, R Wang, AG Day, G Elke, ... Aktuelle Ernährungsmedizin 48 (03), Abstract 23, 2023 | | 2023 |
Andreas BenderLudwig-Maximilians-Universität MünchenVerified email at stat.uni-muenchen.de
